System and method for customisation of media information

ABSTRACT

The present disclosure provides a robust and effective solution to an entity or an organization by enabling the entity to implement a system for increasing relevance and conversion rate of one or more contents. Further, the system delivers to a plurality of users based on user-specific information feed such as location, data usage pattern, recent searches, duration, activities, and the like. The plurality of contents may include mobile phones, tablets, television, Internet, and the like. The system provides for a personalized, customized, and easy to create one or more contents on a plurality of digital platforms devices used by plurality of users. The users include local shop vendors, dealers, and brands to get their product or shop advertised to the potential target audience and increase the reach and conversion rate of the advertisement.

RESERVATION OF RIGHTS

A portion of the disclosure of this patent document contains material,which is subject to intellectual property rights such as, but are notlimited to, copyright, design, trademark, Integrated Circuit (IC) layoutdesign, and/or trade dress protection, belonging to Jio PlatformsLimited (JPL) or its affiliates (hereinafter referred as owner). Theowner has no objection to the facsimile reproduction by anyone of thepatent document or the patent disclosure, as it appears in the Patentand Trademark Office patent files or records, but otherwise reserves allrights whatsoever. All rights to such intellectual property are fullyreserved by the owner.

FIELD OF INVENTION

The present disclosure relates generally to the field of computer visionand digital media. More particularly, the present disclosure provides asystem and a method for facilitating visual attention to a potentialtargeted audience, while increasing the effectiveness of anadvertisement.

BACKGROUND OF THE INVENTION

The following description of related art is intended to providebackground information pertaining to the field of the disclosure. Thissection may include certain aspects of the art that may be related tovarious features of the present disclosure. However, it should beappreciated that this section be used only to enhance the understandingof the reader with respect to the present disclosure, and not asadmissions of prior art.

The world of advertisements is evolving with advancements in technologyand the nature of audience. With the increase in technologyadvancements, new platforms have evolved in entertaining users that hasreduced the attention time span of the users. Modern marketing hasimpacted the time, budget, resources, type, and duration of theadvertisements (ads). The time involved in creating the ads is still amajor concern for many brands. Further, local shop owners cannot affordthe budget for making the ads. Furthermore, availability of relevantresources and data is again a crucial point that is required to haveeffective advertisement and brand marketing for a targeted audience.Contemporary systems and methods provide solutions by automating theentire advertising process. Further, the contemporary systems andmethods provide valuable budgets with minimal human intervention andhigher return of investment. Additionally, an artificial intelligence(AI) engine optimizes the ads for the targeted audience on multiplechannels. As sales and marketing are interconnected, the AI engineautonomously optimizes the marketing campaigns based on customers for aparticular business.

Conventional systems and methods are disclosed for dynamicallyconstructing personalized contextual video programs. In particular, theconventional method includes extracting video metadata from a videoprogram, extracting component metadata from video components stored in amedia object library, extracting viewer preferences, and receivingsynchronization information about the video program. Further, the methodincludes identifying a video program segment susceptible to inserting avideo component, and transmitting the video component to a playbackdevice. Furthermore, the method includes instructions about insertingthe video component in the video program segment. A viewer profile canbe based on demographic information and user behaviour. The videoprogram and the video component can be combined before transmitting thevideo component and instructions to the playback device. A videocomponent can be selected based on an advertiser's preference whereasthe transmitted video component and instructions can be stored as aconstruction list for future use.

Another conventional system and method discloses an improved advertisingwith video ad creatives. The serving of ads with (e.g., on) videodocuments may be improved by estimating a video advertisementperformance using {tag, value} pair keys. Further, the tags may pertainto video advertisements. For example, such estimates may be used indeterminations of whether and/or how to serve a candidate video ad.

However, there remains a challenge to retain the attention span of theaudience on a digital platform. Additional challenges may be anticipatedthat may lead to issues in the way information is delivered to theaudience. Further, challenges may be observed in the way the audienceknows and remembers the product and services in a short span of time.

There is, therefore, a need in the art to provide a system and a methodthat can mitigate the problems associated with the conventional systemsand methods.

OBJECTS OF THE INVENTION

Some of the objects of the present disclosure, which at least oneembodiment herein satisfies are as listed herein below.

It is an object of the present disclosure to provide a system and amethod that improves attention span and interest of an audience for adsdisplayed.

It is an object of the present disclosure to provide a system and amethod that facilitates a lower cost towards the production ofmulti-label, multi-brand ads with a single base media.

It is an object of the present disclosure to provide a system and amethod that facilitates easy and personalized method for local shops,retailers, brands, etc. to advertise products to potential consumers.

It is an object of the present disclosure to provide a system and amethod that reduces the overall time due to the utilization of AI-basedcontent configuration and video generation.

It is an object of the present disclosure to provide a system and amethod that improves the audience-to-customer conversion rate.

It is an object of the present disclosure to provide a system and amethod that facilitates a dynamic, robust, and a cost-efficientapproach.

SUMMARY

This section is provided to introduce certain objects and aspects of thepresent disclosure in a simplified form that are further described belowin the detailed description. This summary is not intended to identifythe key features or the scope of the claimed subject matter.

In an aspect, the present disclosure relates to a system that mayinclude one or more processors operatively coupled to one or morecomputing devices. The one or more processors may be coupled with amemory that stores instructions to be executed by the one or moreprocessors. The one or more processors may be configured to receive oneor more input parameters from the one or more computing devices using aninformation template. The one or more computing devices may beassociated with one or more users and may be connected to the processorthrough a network. The one or more input parameters may be indicative ofone or more contents provided by the one or more users through the oneor more computing devices. Further, the one or more processors mayextract a first set of attributes from the one or more input parameters.The first set of attributes may be indicative of one or more keywordsbased on the one or more contents. Additionally, the one or moreprocessors may extract a second set of attributes based on the first setof attributes. The second set of attributes may be indicative of one ormore categories for the one or more keywords. The one or more processorsmay extract a third set of attributes based on the second set ofattributes, where the third set of attributes may be indicative of oneor more priority rankings for the one or more categories. Based on thefirst set of attributes, the second set of attributes, and the third setof attributes, the one or more processors may generate a predictivemodel through an artificial intelligence (AI) engine. Further, theprocessor may generate one or more media information customizationsbased on the predictive model.

In an embodiment, the one or more techniques used by the AI engine mayinclude one or more text feature extraction techniques and one or moreimage feature extraction techniques to generate the predictive model.

In an embodiment, the one or more input parameters may include any or acombination of a location, a network strength, a band, a data usagehistory, a user profile, and a user subscription.

In an embodiment, the one or more keywords generated by the one or moreprocessors may include any or a combination of a name, a brand, and adescription for the one or more media information customizations.

In an embodiment, the one or more processors may be configured togenerate a template selection, a concept, a credibility, and a potentialscore for the one or more users based on the one or more categories.

In an embodiment, the one or more processors may be configured to usethe potential score for the one or more users and generate the one ormore priority rankings based on the potential score.

In an embodiment, the one or more processors may be configured to useone or more post-processing techniques and generate a visual attentionbased-model through the AI engine for an enhancement of the one or moremedia information customizations.

In an embodiment, the one or more post-processing techniques used by theone or more processors may include any or a combination of a colourenhancement technique and an advertisement positioning technique for theenhancement of the one or more media information customizations.

In an embodiment, the one or more processors may be configured togenerate one or more template cards associated with the visualattention-based model and generate the enhancement of the one or moremedia information customizations based on the one or more templatecards.

In an embodiment, the one or more template cards may include any or acombination of one or more photos, one or more graphics, one or moretransitions, and one or more musical elements for the one or more mediainformation customizations.

In an aspect, the present disclosure generally relates to a method forproviding one or more media information customizations. The method mayinclude receiving, by one or more processors, one or more inputparameters from one or more computing devices using an informationtemplate. The one or more computing devices may be associated with oneor more users and may be connected to the one or more processors througha network. The one or more input parameters may be indicative of one ormore contents provided by the one or more users through the one or morecomputing devices. The method may include extracting, by the one or moreprocessors, a first set of attributes from the one or more inputparameters. The first set of attributes may be indicative of one or morekeywords based on the one or more contents. The method may includeextracting, by the one or more processors, a second set of attributesbased on the first set of attributes. The second set of attributes maybe indicative of one or more categories for the one or more keywords.Further, the method may include extracting, by the one or moreprocessors, a third set of attributes based on the second set ofattributes. The third set of attributes may be indicative of one or morepriority rankings for the one or more categories. The method may includegenerating, by the one or more processors, based on the first set ofattributes, the second set of attributes, and the third set ofattributes, a predictive model through an AI engine. The AI engine maybe configured to use one or more techniques to generate the predictivemodel. Further, the method may include generating, by the one or moreprocessors, the one or more media information customizations based onthe predictive model.

In an embodiment, the method may include using, by the one or moreprocessors, one or more post-processing techniques. Additionally, themethod may include generating, by the one or more processors, a visualattention-based model through the AI engine for an enhancement of theone or more media information customizations.

In an embodiment, the method may include the one or more post processingtechniques used by the one or more processors with any or a combinationof a colour enhancement and an advertisement positioning for theenhancement of the one or more media information customizations.

In an embodiment, the method may include generating, by the one or moreprocessors, one or more template cards associated with the visualattention-based model. Additionally, the method may include generating,by the one or more processors, the enhancement of the one or more mediainformation customizations.

In an embodiment, the method may include generating, by the one or moreprocessors, one or more template cards associated with the visualattention-based model. Additionally, the method may further includegenerating, by the one or more processors, the enhancement of the one ormore media information customizations based on the one or more templatecards.

In an embodiment, the method may include the one or more template cardswith any or a combination of one or more photos, one or more graphics,one or more transitions, and one or more musical elements for the one ormore media information customizations.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated herein, and constitutea part of this disclosure, illustrate exemplary embodiments of thedisclosed methods and systems in which like reference numerals refer tothe same parts throughout the different drawings. Components in thedrawings are not necessarily to scale, emphasis instead being placedupon clearly illustrating the principles of the present disclosure. Somedrawings may indicate the components using block diagrams and may notrepresent the internal circuitry of each component. It will beappreciated by those skilled in the art that disclosure of such drawingsincludes the disclosure of electrical components, electronic componentsor circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary architecture (100) of a proposed system(110), in accordance with an embodiment of the present disclosure.

FIG. 2 illustrates an exemplary representation (200) of the proposedsystem (110), in accordance with an embodiment of the presentdisclosure.

FIG. 3 illustrates an exemplary block diagram representation (300) ofthe proposed system (110), in accordance with an embodiment of thepresent disclosure.

FIGS. 4A-4C illustrate exemplary key components of the proposed system(110), in accordance with an embodiment of the present disclosure.

FIG. 5 illustrates an exemplary computer system (500) in which or withwhich the proposed system (110) may be implemented, in accordance withembodiments of the present disclosure.

The foregoing shall be more apparent from the following more detaileddescription of the disclosure.

BRIEF DESCRIPTION OF INVENTION

In the following description, for the purposes of explanation, variousspecific details are set forth in order to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent, however, that embodiments of the present disclosure may bepracticed without these specific details. Several features describedhereafter can each be used independently of one another or with anycombination of other features. An individual feature may not address allof the problems discussed above or might address only some of theproblems discussed above. Some of the problems discussed above might notbe fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the exemplary embodimentswill provide those skilled in the art with an enabling description forimplementing an exemplary embodiment. It should be understood thatvarious changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the disclosure as setforth.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed but could have additional steps notincluded in a figure. A process may correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to meanserving as an example, instance, or illustration. For the avoidance ofdoubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art. Furthermore, to the extent that theterms “includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, such terms are intendedto be inclusive in a manner similar to the term “comprising” as an opentransition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “anembodiment” or “an instance” or “one instance” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the presentdisclosure. Thus, the appearances of the phrases “in one embodiment” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an”, and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

Referring to FIG. 1 , exemplary network architecture (100) isillustrated in accordance with an embodiment of the present disclosure.As illustrated in FIG. 1 , a plurality of computing devices (104-1,104-2 . . . 104-N) (herein referred as computing devices (104)) may beconnected to a system (110). The computing devices (104) may also beknown as a user equipment (UE) that may include, but not be limited to,a mobile, a laptop, etc. Further, the computing devices (104) mayinclude one or more in-built or externally coupled accessoriesincluding, but not limited to, a visual aid device such as camera, audioaid, a microphone, a keyboard, input devices for receiving input from auser such as touch pad, touch enabled screen, electronic pen, and thelike. It may be appreciated that the computing devices (104) may not berestricted to the mentioned devices and various other devices may beused.

The computing devices (104) may be connected to the system (110) througha network (106). In an exemplary embodiment, the network (106) mayinclude, by way of example but not limitation, at least a portion of oneor more networks having one or more nodes that transmit, receive,forward, generate, buffer, store, route, switch, process, or acombination thereof, etc. One or more messages, packets, signals, waves,voltage or current levels, some combination thereof, or so forth may beincluded by the one or more nodes. The network (106) may include, by wayof example but not limitation, one or more of a wireless network, awired network, an internet, an intranet, a public network, and a privatenetwork. Further, the network (106) may include a packet-switchednetwork, a circuit-switched network, an ad hoc network, aninfrastructure network, a public-switched telephone network (PSTN), acable network, a cellular network, a satellite network, a fibre opticnetwork, some combination thereof.

One or more users (102) (herein referred as users (102)) may provide oneor more input parameters indicative of one or more contents through thecomputing devices (104). In an embodiment, the system (110) may includean AI engine (216) for generating a predictive model using one or moretechniques. The AI engine (216) may be configured to use one or moretechniques and generate one or more media information customizationsbased on the predictive model. The one or more media customizations mayinclude visual attention-based advertisement enhancement to capture theattention of the users (104).

FIG. 2 illustrates an exemplary representation (200) of the proposedsystem (110), in accordance with an embodiment of the presentdisclosure.

Referring to FIG. 2 , the system (110) may comprise one or moreprocessor(s) (202). The one or more processor(s) (202) may beimplemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,logic circuitries, and/or any devices that process data based onoperational instructions. Among other capabilities, the one or moreprocessor(s) (202) may be configured to fetch and executecomputer-readable instructions stored in a memory (204) of the system(110). The memory (204) may be configured to store one or morecomputer-readable instructions or routines in a non-transitory computerreadable storage medium, which may be fetched and executed to create orshare data packets over a network service. The memory (204) may compriseany non-transitory storage device including, for example, volatilememory such as random-access memory (RAM), or non-volatile memory suchas erasable programmable read only memory (EPROM), flash memory, and thelike.

In an embodiment, the system (110) may include an interface(s) (206).The interface(s) (206) may comprise a variety of interfaces, forexample, interfaces for data input and output devices, referred to asinput/output (I/O) devices, storage devices, and the like. Theinterface(s) (206) may facilitate communication for the system (110).The interface(s) (206) may also provide a communication pathway for oneor more components of the system (110). Examples of such componentsinclude, but are not limited to, processing engine(s) (208) and adatabase (210).

The processing engine(s) (208) may be implemented as a combination ofhardware and programming (for example, programmable instructions) toimplement one or more functionalities of the processing engine(s) (208).In examples described herein, such combinations of hardware andprogramming may be implemented in several different ways. For example,the programming for the processing engine(s) (208) may beprocessor-executable instructions stored on a non-transitorymachine-readable storage medium and the hardware for the processingengine(s) (208) may comprise a processing resource (for example, one ormore processors), to execute such instructions. In the present examples,the machine-readable storage medium may store instructions that, whenexecuted by the processing resource, implement the processing engine(s)(208). In such examples, the system (110) may comprise themachine-readable storage medium storing the instructions and theprocessing resource to execute the instructions, or the machine-readablestorage medium may be separate but accessible to the system (110) andthe processing resource. In other examples, the processing engine(s)(208) may be implemented by electronic circuitry.

Referring to FIG. 2 , the processing engine(s) (208) may include one ormore engines selected from any of a signal acquisition engine (212), anextraction engine (214), an AI engine (216) and other engine(s) (218).In an embodiment, the signal acquisition engine (212) may receive one ormore input parameters from computing devices, such as computing devices(104) of FIG. 1 using an information template. The one or more inputparameters may be indicative of one or more contents provided by users,such as users (102) of FIG. 1 through the computing devices (104). Theone or more input parameters comprise any or a combination of alocation, a network strength, a band, a data usage history, a userprofile, and a user subscription.

In an embodiment, the extraction engine (214) may extract a first set ofattributes from the one or more input parameters and store the first setof attributes in the database (210). The first set of attributes may beindicative of one or more keywords based on the one or more contents. Inan embodiment, the one or more keywords may comprise any or acombination of a name, a brand, and a description for one or more mediainformation customizations.

In an embodiment, the extraction engine (214) may extract a second setof attributes based on the first set of attributes and store the secondset of attributes in the database (210). The second set of attributesmay be indicative of one or more categories for the one or morekeywords. In an embodiment, the extraction engine (214) may extract athird set of attributes based on the second set of attributes and storethe third set of attributes in the database (210). The third set ofattributes may be indicative of one or more priority rankings for theone or more categories. In an embodiment, based on the first set ofattributes, the second set of attributes, and the third set ofattributes, the one or more processor(s) (202) may generate a predictivemodel through the AI engine (216) that uses one or more techniques.Additionally, the one or more processor(s) (202) may generate the one ormore media information customizations based on the predictive model.Further, the one or more processors (202) may generate a templateselection, a concept, a credibility, and a potential score for the users(102) based on the one or more categories.

In an embodiment, the one or more techniques used by the AI engine (216)may comprise one or more text feature extraction techniques and one ormore image feature extraction techniques to generate the predictivemodel. In an embodiment, the AI engine (216) may further include aSMART-AD card (SAC) module (306) to generate advertisement cards throughpredictive analysis. This predictive analysis may contain information ofkeywords, keyword's concept, and a priority score that gives aconfidence about the ad shown to the users (102).

In an embodiment, the other engine(s) (218) may include an Infocardmodule, an Ad card generation module, a base media module, and a visualattention-based ad colour enhancement and ad placement module (310).

FIG. 3 illustrates an exemplary block diagram representation (300) ofthe proposed system, in accordance with an embodiment of the presentdisclosure.

As illustrated in FIG. 3 , the proposed system may start with anInfocard module (302). The Infocard module (302) may produce informationabout a potential product, service, item, or the like, based on theinformation provided by users, such as the users (102) of FIG. 1 . Thisinformation is further given to an Ad card generation module (304) toproduce a Smart-Ad card (SAC). The Smart-Ad card module (306) mayprovide a modified version of a media template that can be given to mapwith base media. The SAC (306) can be then mapped with a base mediamodule (308) and a visual attention-based ad colour enhancement and adplacement module (310) to enhance the engagement of the users (102) withan advertisement shown on their respective computing devices (104).

Further, the Infocard module (302) may generate keywords based on theinformation provided by the users (102). The Ad card generation module(304) may generate a SAC that may be combined with the base media module(308) to provide the input to the visual attention-based AD colourenhancement and AD placement module (310). The visual attention-based ADcolour enhancement and placement module (310) may enhance the engagementof the users (102) with the advertisement shown on their respectivecomputing devices (104). Hence, an advertisement power by artificialintelligence (AI) with attention (ADAIA) (312) may be available.

FIGS. 4A-4C illustrate exemplary key components of the proposed system(110), in accordance with an embodiment of the present disclosure. Asillustrated, FIG. 4A shows a system architecture of an Infocard module,such as the Infocard module (302) of FIG. 3 . In an embodiment, theInfocard module (302) may be responsible for generation of keywords onwhich an advertisement template-based card is to be generated. Referringto FIG. 4A, the Infocard module (302) may comprise at least two blocks,a first AI-powered block (402) that may focus on the desired set ofoutcomes from the users (102), and a second AI-powered block (404) thatmay focus on AI-based recommendations for better and more accuratepredictions of user's needs, potential, and willingness to convert theadvertisement. The desired block set gives a stack of keywordscomprising a set of words and categories as output. The stack ofkeywords may be provided in the form of top I-ranked items which may begiven to further blocks. In an embodiment, the one or more keywordsgenerated may comprise any or a combination of a name, a brand, and adescription for one or more media information customizations. In anembodiment, categories may refer to a class with similar featuredistribution. All the information may contribute towards providing theone or more keywords along with the credibility of the users (102) andthe potential score of the users (102).

FIG. 4B illustrates a SAC module (306) responsible for providing amodified version of a media template that can be given to map with basemedia. Based on the information provided by an Infocard module (412), atemplate selection, concept, credibility of the users (102), andpotential score of the users (102), an advertisement is generated. TheSAC module (306) consists of at least two basic levels, where one takesup information from the Infocard module (412) and gives the informationto the next stage for ad generation. Based on category information, theAd card generation (420), and specifically, the keyword module (414) maychoose artifacts that may include photos, music, transition, graphic,and the like. This may be further configured based on the keyword andpriority information. This generated template card may be further mappedto the base media to generate a SAC module (306). The category module(416) may include the template for the keywords, and the priority module(418) may include the credibility for the generated keywords andtemplate.

In an exemplary embodiment, users (102) may be referred to as a shop,brand owners, dealers, etc. and internet consumers may be referred as anaudience. A person of ordinary skill in the art will understand that aservice provider may be referred as the source for a base video. In anembodiment, the proposed system starts from users (102) who put somebasic details required to create custom advertisements like shop name,logo of the shop, any product, offers, new openings, brands, etc. Aftergetting the required information for creating personalised ads, theinformation is used to determine the type of the advertisement and anAI-based recommendation system is used. In an embodiment, the AI-basedrecommendation system is used based on the domain of application. Afterdetermining the type of the advertisement, templates are selected andconfigured. Further, audience specific data such as, but not limited to,location, data usage pattern, recent searches, duration, activities,etc. are used to give predictive analysis about targeted audience and atarget product or advertiser along with priority score. The audiencespecific data may also be used to configure the advertisement content toincrease information acceptance by the audience, which collaborativelygenerates the SAC (306).

FIG. 4C illustrates an exemplary block diagram representation of visualattention-based ad colour correction and placement. Post mapping of thewith the base media, as discussed above with reference to FIGS. 3, 4A,and 4B, an advertisement is ready. Now, in next stage, the SAC with basemedia is given for post-processing which may help in enhancing theattention of the users (102) towards the advertisement.

In an embodiment, one or more post-processing techniques may and used togenerate a visual attention-based model through an AI engine (216), suchas the AI engine (216) of FIG. 2 , for an enhancement of one or moremedia information customizations.

In an embodiment, the one or more post processing techniques maycomprise any or a combination of a colour enhancement technique and anad positioning technique for the enhancement of the one or more mediainformation customizations.

In an exemplary embodiment, an Ad placement module (422) may provide thead to a visual enhancement module (424) that provides colour correctionfor the ad. A visual attention-based model (426) may determine theeyeball position of a user on a screen. In an embodiment, colourcorrection may be performed at the SAC. After this stage, theadvertisement is ready for the target audience and target products orservices given by brands, shops, distributors, and the like.

In an embodiment, one or more template cards associated with the visualattention-based model may be generated. In an embodiment, enhancement ofthe one or more media information customizations may be generated basedon the one or more template cards. In an embodiment, the one or moretemplate cards may comprise any or a combination of one or more photos,one or more graphics, one or more transitions, and one or more musicalelements for the one or more media information customizations.

Further, in an embodiment, t may be a threshold value for a scoregenerated by the visual attention-based model (426). If the score isabove the threshold value (428), it will be accepted as an output orelse a feedback path may be followed. The add placement andre-colourisation may be performed to improve the ADAIA score (430).

FIG. 5 illustrates an exemplary computer system (500) in which or withwhich the proposed system (110) may be implemented, in accordance withembodiments of the present disclosure. As shown in FIG. 5 , the computersystem (500) may include an external storage device (510), a bus (520),a main memory (530), a read-only memory (540), a mass storage device(550), communication port(s) (560), and a processor (570). A personskilled in the art will appreciate that the computer system (500) mayinclude more than one processor and communication port. The processor(570) may include various modules associated with embodiments of thepresent disclosure. The communication port(s) (560) may be any of anRS-252 port for use with a modem-based dialup connection, a 10/100Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, aserial port, a parallel port, or other existing or future ports. Thecommunication port(s) (560) may be chosen depending on a network, such aLocal Area Network (LAN), Wide Area Network (WAN), or any network towhich the computer system (500) connects. The main memory (530) may be aRandom-Access Memory (RAM), or any other dynamic storage device commonlyknown in the art. The read-only memory (540) may be any static storagedevice(s) e.g., but not limited to, a Programmable Read Only Memory(PROM) chips for storing static information e.g., start-up or basicinput/output system (BIOS) instructions for the processor (570). Themass storage device (550) may be any current or future mass storagesolutions, which can be used to store information and/or instructions.

The bus (520) may communicatively couple the processor(s) (570) with theother memory, storage, and communication blocks. The bus (520) may be,e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X)bus, Small Computer System Interface (SCSI), universal serial bus (USB)or the like. The bus (520) may further include connecting expansioncards, drives, and other subsystems as well as other buses, such a frontside bus (FSB), which connects the processor (570) to the computersystem (500).

Optionally, operator and administrative interfaces, e.g. a display,keyboard, and a cursor control device, may also be coupled to the bus(520) to support direct operator interaction with the computer system(500). Other operator and administrative interfaces may be providedthrough network connections connected through the communication port(s)(560). Components described above are meant only to exemplify variouspossibilities. In no way should the aforementioned exemplary computersystem (500) limit the scope of the present disclosure.

Thus, the present disclosure provides for a unique and efficient systemto provide keywords, description, and priority ranking based onuser-specific information and recommendations. The system can beAI-triggered and modified advertisement content—Artificial Intelligencealgorithms/methods that cover deep-learning, machine learning,reinforcement learning, or any other domain which is part of AI and canbe used for predictive analysis. The predictive analysis may be based onthe type of data being used, type of advertisement, and targetedaudience. The system may further facilitate visual attention-basedcolour correction for improving enhancing attention of the audience.

While considerable emphasis has been placed herein on the preferredembodiments, it will be appreciated that many embodiments can be madeand that many changes can be made in the preferred embodiments withoutdeparting from the principles of the disclosure. These and other changesin the preferred embodiments of the disclosure will be apparent to thoseskilled in the art from the disclosure herein, whereby it is to bedistinctly understood that the foregoing descriptive matter to beimplemented merely as illustrative of the disclosure and not aslimitation.

ADVANTAGES OF THE INVENTION

The present disclosure provides a system that improves the attentionspan and interest of the audience for the advertisement displayed.

The present disclosure provides a system and a method that facilitates alower cost towards the production of multi-label, multi-brand ads with asingle base media.

The present disclosure provides a system and a method that facilitateseasy and personalized method for local shops, retailers, brands, etc. toadvertise their products to potential consumers.

The present disclosure provides a system and a method that reduces theoverall time due to the utilization of AI-based content configurationand video generation.

The present disclosure provides a system and a method that improves theaudience-to-customer conversion rate.

The present disclosure provides a system and a method that facilitates adynamic, robust, and a cost-efficient approach.

We claim:
 1. A system (110) for providing one or more media informationcustomizations, said system (110) comprising: one or more processors(202) operatively coupled to one or more computing devices (104), theone or more processors (202) coupled with a memory (204), wherein saidmemory (204) stores instructions which when executed by the one or moreprocessors (202) causes the one or more processors (202) to: receive oneor more input parameters from the one or more computing devices (104)using an information template, wherein the one or more computing devices(104) are associated with one or more users (102) and are connected tothe one or more processors (202) through a network (106), and whereinthe one or more input parameters are indicative of one or more contentsprovided by the one or more users (102) through the one or morecomputing devices (104); extract a first set of attributes from the oneor more input parameters, wherein the first set of attributes areindicative of one or more keywords based on the one or more contents;extract a second set of attributes based on the first set of attributes,wherein the second set of attributes are indicative of one or morecategories for the one or more keywords; extract a third set ofattributes based on the second set of attributes, wherein the third setof attributes are indicative of one or more priority rankings for theone or more categories; based on the first set of attributes, the secondset of attributes, and the third set of attributes, generate apredictive model through an artificial intelligence (AI) engine (216),wherein the AI engine (216) is configured to use one or more techniques;and generate the one or more media information customizations based onthe generated predictive model.
 2. The system (110) as claimed in claim1, wherein the one or more techniques used by the AI engine (216)comprise one or more text feature extraction techniques and one or moreimage feature extraction techniques to generate the predictive model. 3.The system (110) as claimed in claim 1, wherein the one or more inputparameters comprise any or a combination of a location, a networkstrength, a band, a data usage history, a user profile, and a usersubscription.
 4. The system (110) as claimed in claim 1, wherein the oneor more keywords generated by the one or more processors (202) compriseany or a combination of a name, a brand, and a description for the oneor more media information customizations.
 5. The system (110) as claimedin claim 1, wherein the one or more processors (202) are configured togenerate a template selection, a concept, a credibility, and a potentialscore for the one or more users (102) based on the one or morecategories.
 6. The system (110) as claimed in claim 5, wherein the oneor more processors (202) are configured to use the potential score forthe one or more users (102) and generate the one or more priorityrankings based on the potential score.
 7. The system (110) as claimed inclaim 1, wherein the one or more processors (202) are configured to useone or more post-processing techniques and generate a visualattention-based model through the AI engine (216) for an enhancement ofthe one or more media information customizations.
 8. The system (110) asclaimed in claim 7, wherein the one or more post-processing techniquesused by the one or more processors (202) comprise any or a combinationof a colour enhancement technique and an advertisement positioningtechnique for the enhancement of the one or more media informationcustomizations.
 9. The system as claimed in claim 7, wherein the one ormore processors (202) are configured to generate one or more templatecards associated with the visual attention-based model, and generate theenhancement of the one or more media information customizations based onthe one or more template cards.
 10. The system (110) as claimed in claim9, wherein the one or more template cards comprise any or a combinationof one or more photos, one or more graphics, one or more transitions,and one or more musical elements for the one or more media informationcustomizations.
 11. A method for providing one or more media informationcustomizations, said method comprising: receiving, by one or moreprocessors (202), one or more input parameters from one or morecomputing devices (104) using an information template, wherein the oneor more input parameters are indicative of one or more contents providedby one or more users (102) through one or more computing devices (104);extracting, by the one or more processors (202), a first set ofattributes from the one or more input parameters, wherein the first setof attributes are indicative of one or more keywords based on the one ormore contents; extracting, by the one or more processors (202), a secondset of attributes based on the first set of attributes, wherein thesecond set of attributes are indicative of one or more categories forthe one or more keywords; extracting, by the one or more processors(202), a third set of attributes based on the second set of attributes,wherein the third set of attributes are indicative of one or morepriority rankings for the one or more categories; generating, by the oneor more processors (202), based on the first set of attributes, thesecond set of attributes, and the third set of attributes, a predictivemodel through an artificial intelligence (AI) engine (216), wherein theAI engine (216) is configured to use one or more techniques; andgenerating, by the one or more processors (202), the one or more mediainformation customizations based on the predictive model.
 12. The methodas claimed in claim 11, comprising using, by the one or more processors(202), one or more post-processing techniques and generating a visualattention based-model through the AI engine (216) for an enhancement ofthe one or more media information customizations.
 13. The method asclaimed in claim 12, wherein the one or more post-processing techniquesused by the one or more processors (202) comprise any or a combinationof a colour enhancement and an advertisement positioning for theenhancement of the one or more media information customizations.
 14. Themethod as claimed in claim 12, comprising, generating by the one or moreprocessors (202), one or more template cards associated with the visualattention based-model, and generating the enhancement of the one or moremedia information customizations based on the one or more templatecards.
 15. The method as claimed in claim 14, wherein the one or moretemplate cards comprise any or a combination of one or more photos, oneor more graphics, one or more transitions, and one or more musicalelements for the one or more media information customizations.
 16. Auser equipment (UE) (104) for providing one or more media informationcustomizations, said UE (104) comprising: one or more processorscommunicatively coupled to one or more processors (202) comprised in asystem (110), the one or more processors coupled with a memory, whereinsaid memory stores instructions which when executed by the one or moreprocessors causes the UE (104) to: transmit one or more input parametersto the one or more processors (202) using an information template,wherein the UE (104) is associated with one or more users (102) and isconnected to the one or more processors (202) through a network (106);wherein the one or more processors (202) are configured to: receive theone or more input parameters from the UE (104) using the informationtemplate, wherein the one or more input parameters are indicative of oneor more contents provided by the one or more users (102) through the UE(104); extract a first set of attributes from the one or more inputparameters, wherein the first set of attributes are indicative of one ormore keywords based on the one or more contents; extract a second set ofattributes based on the first set of attributes, wherein the second setof attributes are indicative of one or more categories for the one ormore keywords; extract a third set of attributes based on the second setof attributes, wherein the third set of attributes are indicative of oneor more priority rankings for the one or more categories; based on thefirst set of attributes, the second set of attributes, and the third setof attributes, generate a predictive model through an artificialintelligence (AI) engine (216), wherein the AI engine (216) isconfigured to use one or more techniques; and generate the one or moremedia information customizations based on the generated predictivemodel.